📋 Tender Overview
There is a significant gap between technological capabilities and executives readiness to apply them. Modern AI, particularly deep learning, relies heavily on graphic or tensor processors, whose architecture is perfectly suited for parallel computations required in AI. Outdated infrastructures typically lack these processors. Legacy systems often dont support horizontal scaling. AI tasks demand elasticity -- the ability to quickly allocate vast resources for training and then scale down for inference. Effective AI operations necessitate centralized and well-organized data platforms. For real-time AI solutions (such as chatbots), short delay timing is critically important, and outdated networking equipment can increase delay. Modern frameworks for AI require current operating system versions, drivers, and libraries. Installing them on obsolete OS may be impossible. Old systems complicate software updates, potentially creating vulnerabilities, especially dangerous when AI processes confidential data. Attempting to squeeze AI onto outdated hardware leads to skyrocketing electricity bills, cooling costs, and downtime awaiting results. Major corporations increasingly release their AI models as open source, which is a global trend.
